Separation of thia-arenes and aza-arenes from polycyclic aromatics in snowpack samples from the Athabasca oil sands region by GC×GC/ToF-MS
Bibliographic record
Abstract
Comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/ToF-MS) was used for the analysis of thia-arenes and aza-arenes in standard mixtures containing 45 polycyclic aromatic compounds (PACs), and in the aromatic fraction of snowpack samples collected from the Athabasca oil sands area of Alberta, Canada. The GC columns used included a shape-selective liquid-crystalline stationary phase (LC-50) and a high-polarity ionic-liquid stationary phase (SLB-IL60), both in the first dimension. A fast diffusion and low-polarity nano-stationary phase (NSP-35) and a mid-polarity stationary phase (Rxi-17), were used in the second dimension, respectively. Both configurations showed good repeatability for retention times in the first and second dimensions, peak areas and peak heights. Instrument detection limits ranged from 0.5 to 10 pg µL−1 for both configurations. In general, the LC-50×NSP-35 configuration favoured the separation of isomeric compounds by using more of the 2D chromatographic space available (>80%), particularly for compounds with molecular mass >160 u. Therefore, LC-50×NSP-35 was recommended for the analysis of thia-arenes and alkylated PAHs in environmental samples collected from the oil sands area. Alternatively, the SLB-IL60×Rxi-17 configuration favoured molecular similarity grouping over isomer separation. This was observed for a group of aza-arenes (i.e. carbazoles, benzo[a]carbazoles and alkylated-derivatives), which were resolved from other PACs and from the sample matrix. The risk of false positives and overestimations in the analysis of thia-arenes, aza-arenes and alkylated PACs in one-dimension GC/MS was explored and further reduced by using GC×GC/ToF-MS with LC-50×NSP-35 and SLB-IL60×Rxi-17.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".